Organised
crime’s infiltration in the legitimate private economy: a network analysis
approach Stefano Gurciullo, Computer Science Department and School of Public
Policy, University College London It is
estimated that Italian Mafias registered €135 billion in profits only in 2010.
Part of this huge amount of money – coming mostly from the drugs, prostitution
and arms illicit markets – is often used to invest into legitimate private
economies. As a consequence, the affected economies destabilise, become
entrenched with violent forms of competition and are bound to stagnation.
Criminal organisations’ infiltration in the economy is not an Italian oddity,
but a global security issue. The Indian D-Company possesses a trading company
in Dubai and several construction companies in the Gulf, while the Japanese
Yakuza heavily invests in Asian stock markets. Nonetheless,
few are the attempts to uncover the patterns followed by criminal organisations
in their business ventures. The reason lays mostly in the poor availability of
data on criminal activity, or in the highly risky task of gather it.

This
presentation partially fills this gap thanks to access to information about the
Sicilian Mafia in a city. More specifically, it tries to analyse the nature and
extent of criminal infiltration into the legitimate private economy of the
case-study using social network techniques. The research demonstrates that
sectors with a high degree of centrality and comprising fewer firms are the
most vulnerable to this kind of security threat. It also shows that centrality
is also the key criterion that makes a firm sensitive to infiltration, provided
it belongs to a susceptible economic sector.

Such
conclusions are reached through a four-step analysis. The first section briefly
deals with the theoretical groundings. The research question is contextualised
by drawing on relevant economic, political and criminological literature. A
model that explains why and how they infiltrate economy is developed.

The second
section presents the case-study used to provide at least a partial answer to
the research question. The empirical object of analysis is the local private
economy of a Sicilian city within a confined period, year 2002. The basic
characteristics of the economy are described, together with the Mafioso
activity within it.

The third
section tests two theoretical predictions using network analysis. A one-mode,
non-directional network of the economic sectors active in the case-study is
constructed, thus showing both in a graphical and analytical way how and to
what extent organised crime has infiltrated into the case-study.

The fourth
and most crucial section discusses the robustness of the results and the policy
implications of the research. It is argued that this method can potentially be
used by intelligence operators to monitor criminal organisations’ economic
activities, and to estimate what economic activities or agents are more
vulnerable to the threat. Furthermore, it is shown that the instrument is
scalable, that is, it can elaborate information about economies of any size (be
they international, national or local), and is easily updatable when new
intelligence is gathered.

Over the last decade, there
has been a shift in policing among the industrialized nations. Several factors
have moved police services towards an intelligence-led, data-driven approach to
risk mitigation and operational decision-making. A desire for intelligence to
facilitate and improve decision-making is not just attractive at local levels
of policing. Given the top-down nature of intelligence-led policing, some of
the enthusiasm for the development of intelligence-led decision-making from
national governments also may have stemmed from the potential to integrate
national priorities into the local public safety setting. Numerous national
intelligence models are designed with this process in mind, seeking to offer
local police a mechanism not only to identify and highlight local issues, but
to do so hand-in-hand with a central government’s priorities. The National Intelligence
Model is among a number of models that attempt this integration. Among the many
questions that arise from this are issues about the universality of organized
crime group characteristics: do intelligence analysts perceive them similarly
across all levels of the police organization, from national the local?

As financial constraints
have resulted in a need to focus scarce resources on a limited number of only
the most noxious crime groups, intelligence agencies have developed mechanisms
to triage and identify the most successful groups and the groups which pose the
greatest harm. These mechanisms are often based on expert judgment of
intelligence officers and specialists from the field; however, these risk
assessment methodologies have rarely been evaluated in a systematic manner. The
current study makes a contribution to closing this gap by examining the
perceptions of organized crime gang capabilities using data from a survey of
intelligence officers at the local, regional and national levels. Differences
in their perceptions of what constitutes the characteristics of successful
gangs are illuminating and likely indicative of different priorities for police
in the hierarchical system, as well as the constraints of observing the
organized crime problem through a particular lens.

In this presentation I
present survey results using the RCMP Sleipnir framework as a foundation for a
Q-Sort survey regarding the characteristics of organized crime group success.
The survey was delivered to over 150 criminal intelligence specialists at an
international conference in 2011. Results show that perception of organized
crime group success varies by nationality, as well as by the analyst’s level
within the hierarchy of the law enforcement structure (local, state, national).
Potential reasons for these outcomes are discussed. The findings raise
questions about reliability and validity of organized crime risk assessments,
and are directly applicable to thinking about national-to-local priorities
within the National Intelligence Model framework.